In my current research I'm working on an optimization problem over a domain of matrices; I'm trying to convert this optimization problem into it's convex form. In order to do so I need a similar relation for negative definite matrices.
Can the Schur complement lemma be extended to the case of negative definite matrices? And if so, how?
Namely, is it true that for a matrix $M$ of the same structure we have $M\leq 0$ iff $C<0$ and $A-BC^{-1}B^T\leq 0$?

Another similar but different problem I have regards to the following non-convex (nor linear) constraint: $A-BC^{-1}B^T\leq 0$ and $A\geq 0$. Is there some way such constraints can be converted to an equivalent constraint which is linear in these variables? For example the two constraints $A-BC^{-1}B^T\geq 0$ and $C> 0$ can be simply converted to $M\geq 0$ using the Schur complement lemma. The new constraint $M\geq 0$ is equivalent to the two old ones and is indeed linear in the matrices $A,B,C$. I'm looking for a way to do something similar to this for my case.

Ohh I'm sorry, I accidentally mixed the two version of the lemma. I've edited the original question and now I think it's fine; thanks for the remark! So you say that $M\leq 0$ iff $C<0$ and $A-BC^{-1}B^T$ holds? Any ideas regarding the second question? Thanks again!
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Ziv GoldfeldJan 4 '13 at 20:34

Thank you. By the way, why is it important to have $C>0$ (or $C<0$ for the negative version) in order to use the lemma? Why isn't $C\neq 0$ enough? I ask it since we do not demand anything regarding the matrices $A$ and $B$ as far as non-negativeness, but we do demand it for $C$. Why is that?
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Ziv GoldfeldJan 5 '13 at 21:58

1

Good point, M≥0 does not imply C>0, take, e.g. M=0, that's a positive semi-definite matrix. It implies only A≥0 and C≥0. But if one of A and C is stricly positive and therefore invertible, them M≥0 is equivalent to the other condition involving its inverse. See the Wikipedia article.
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Uwe FranzJan 5 '13 at 23:17